Очередная инструкция на случай амнезии.

YT-DLP - БАЗИРОВАННАЯ тулза для скачивания контента с медиахостингов, и многие проги ей завидуют хранят под капотом именно её ядро. Оседлав этого жеребца, смотришь на других наездников, как на бедолаг с раздолбанным очком. Приступим.

1. yt-dlp работает в связке с ffmpeg, поэтому скачиваем свежие релизы первого и второго и распаковываем исполняемые файлы в одну папку.

2. Поскольку yt-dlp работает через CLI, чтобы вызывать его из любого места добавим путь к программе в переменную среды Windows PATH.

🔊 Да, я знаком с библиотекой FFmpeg — это мощный инструмент для работы с мультимедиа, который позволяет кодировать, декодировать, конвертировать, изменять и обрабатывать аудио и видео файлы. Вот несколько полезных команд, которые могут тебе пригодиться:

ffmpeg -i input.mp4 output.avi

Good question! I am collecting human data on how quantization affects outputs. See here for more information: ggml-org/llama.cpp#5962

In the meantime, use the largest that fully fits in your GPU. If you can comfortably fit Q4_K_S, try using a model with more parameters.

See the wiki upstream: https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix

Works only when running as same user whose credentials we are dumping

C:\Users\Alex\Desktop> vaultcmd /list

C:\Users\Alex\Desktop> vaultcmd /listcreds:"<vault-name>" /all

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

Brave is an excellent, privacy-focused browser, but its default configuration comes packed with extra features that many users consider bloat. This guide will walk you through how to get rid of the bloat, hide the ads, and disable background services for a clean, minimal browsing experience.

The default start page and toolbars are crowded with Brave "cards," news feeds, and native crypto shortcuts. Fortunately, the surface-level clutter is easy to hide directly from the UI.

• Clean the Start Page: Open a new tab, click the Customize button in the bottom right corner, and toggle off Brave News, Cards, and any unwanted background images or stats.
• Clean the Toolbar: Right-click on the Brave Rewards, Brave Wallet, Brave VPN, and Leo AI icons located next to the URL bar, and select Hide.

Your Mac has a GPU. Your Mac has RAM. Why are you paying someone else to think?

This guide gets you a fully local agentic coding setup: Claude Code talking to Qwen 3.5-35B-A3B via llama.cpp, all running on your Apple Silicon Mac. No API keys. No cloud. No surprise invoices. Just you, your M-series chip, and 35 billion parameters doing your bidding on localhost.

Based on this article.

Recommended to install Waydroid in a brand new Ubuntu install. Waydroid needs a custom linux kernel. Actually just needs latest kernel for WSL2 with just these changes before compiling:

CONFIG_ANDROID_BINDER_IPC=y
CONFIG_ANDROID_BINDER_DEVICES="binder,hwbinder,vndbinder"